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High Precision Clock Bias Prediction Model in Clock Synchronization System

机译:时钟同步系统中的高精度时钟偏差预测模型

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摘要

Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the first-order greymodelwith one variable (GM(1, 1)) model is proposed. In the traditionalGM(1, 1) model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO) is used to optimize GM(1, 1) model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%similar to 76.7%.
机译:时间同步是分布式系统提供的许多服务的基本要求。通过时间信号进行时钟校准是实现分布式系统中使用的时钟之间同步的常用方法。对时间信号传输的干扰或设备故障可能会导致时间同步失败。为了解决这个问题,在时钟校准系统中并联了一个时钟偏差预测模块。为了提高时钟偏差预测的精度,提出了带有一个变量(GM(1,1))模型的一阶灰色模型。在传统的GM(1,1)模型中,由最小二乘准则确定的参数组合不是最优的。因此,粒子群优化(PSO)用于优化GM(1,1)模型。同时,为了避免粒子群优化算法陷入局部优化问题并提高效率,提出了双子群化和非线性惯性权重减小的机制。为了测试改进模型的精度,我们设计了时钟校准实验,其中时间信号分别通过无线电和有线信道传输。在实验中获得的时钟偏差的基础上建立了改进的模型。结果表明,改进后的模型在精度和稳定性上均优于其他模型。改进模型的精度提高了66.4%,与76.7%相似。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第11期|1813403.1-1813403.6|共6页
  • 作者单位

    Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Peoples R China;

    Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Peoples R China;

    Chinese Peoples Liberat Army, Unit 95425, Qujing 655000, Peoples R China;

    Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Peoples R China;

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